28th November 2017

Some background

25,000 genes

200 compatibility genes

6 class I HLA genes

Who, what and when

1936 Peter Gorer

1940s and WWII

1958 HLA gene identified

Thousands have been idenitified to date

1986 Alain Townsend

What HLA proteins are showing the immune system

1987 Pamela Bjorkman

The structure of the HLA protein

1990 to the present

The peptide loading complex, peptide trimmers etc.

Recap

The known knowns

  • There are thousands of these HLA genes
  • We each have up to six
  • HLA proteins present peptides
  • And that's how CD8+ T-cells know what is going inside cells

Completing the Rumsfeld triplet

  • We don't know the mechanism of peptide generation
  • We don't know the mechanism of peptide selection by HLA
  • We don't know what T-cells are seeing at any given time
  • We don't know what we don't know

ImmunoSense Overview

Objectives

  • Characterise the HaCaT immunopeptidome \(\pm\)DNCB.
  • Characterise HaCaT protein turnover/abundance \(\pm\)DNCB.
  • Infer relationship(s) between DNCB, peptidome, protein turn over and protein abundance.
  • Identify (and test) candidate immunogenic peptides.

Method development

Capturing MHC I peptides | Observing protein turnover

Peptidome

MHC I motifs

Peptidome results 1

Database identified : STTGH(C)VHMR

De novo identified : YMDVEE(K)LLF

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Experimental next steps

Complete HaCaT dataset | Start HLA-A2 transfectant dataset

Method development

Quantifying our uncertainty

Peptidome areas of uncertainty

  • How many peptide species are there per treatment?
  • Is the size of the peptidome repertoire different between treatments?
  • Can we estimate the probability that a peptide is unique to one treatment?

Paul A. Smith, S3RI University of Southampton

Protein turnover

Binary general linear model

dat_turnover[c(1:5,902:907),1:3]
## # A tibble: 11 x 3
##    Peptide Treatment Turnover.Rate
##      <int>     <chr>         <dbl>
##  1       1   Control         0.149
##  2       0   Control         0.513
##  3       0   Control         0.491
##  4       1   Control         0.614
##  5       1   Control         0.445
##  6       0      DNCB         0.111
##  7       1      DNCB         0.794
##  8       1      DNCB         0.704
##  9       0      DNCB         0.268
## 10       0      DNCB         0.393
## 11       1      DNCB         0.802

Protein turnover

Binary general linear model

\(Y_i = \beta_0 + \beta_1.X_i + \epsilon_i\)

\(X_i\) : protein turnover rate for protein \(X\).

\(Y_i\) : probability of observing peptide from protein \(X\) at the cell surface.

\(\beta_0\) : expected value of \(Y_i\) under given treatment.

\(\beta_1\) : increase in probability of \(Y_i\) due to increase in turnover rate.

\(\epsilon_i\) : is the residual error term.

Models for \(\beta_0\) and \(\beta_1\)

Summary

Complete HaCaT dataset | Repeat with HLA-A2 transfectant dataset